I am an independent researcher focused on Autonomous Vehicle (AV) planning. I am releasing the paper, code, and weights for a project called . It is a conditional latent diffusion model (LDM) for generating multi-modal, long-horizon driving trajectories. The main goal was to see how much performance could be extracted from a generative model using a single consumer GPU (RTX 3090), rather than relying on massive compute clusters.Most standard motion planners use deterministic regression (Behavioral Cloning) to predict a single path. In urban environments, like unprotected left turns, there is rarely one “correct” path. This often leads to “mode averaging” where the model produces an unsafe path in the middle of two valid maneuvers. Generative models like diffusion handle this multimodality …
I am an independent researcher focused on Autonomous Vehicle (AV) planning. I am releasing the paper, code, and weights for a project called . It is a conditional latent diffusion model (LDM) for generating multi-modal, long-horizon driving trajectories. The main goal was to see how much performance could be extracted from a generative model using a single consumer GPU (RTX 3090), rather than relying on massive compute clusters.Most standard motion planners use deterministic regression (Behavioral Cloning) to predict a single path. In urban environments, like unprotected left turns, there is rarely one “correct” path. This often leads to “mode averaging” where the model produces an unsafe path in the middle of two valid maneuvers. Generative models like diffusion handle this multimodality well but are usually too slow for real-time robotics.To keep the model efficient while maintaining high accuracy, I implemented the following: Instead of running the diffusion process on the raw waypoints (160 dimensions for 8 seconds), the trajectories are projected into a 16-dimensional latent space via PCA. This captures over 99.9 percent of the variance and makes the denoising task much easier.Transformer-based StateEncoder: A Transformer architecture fuses history, surrounding agent states, and map polylines into a scene embedding. This embedding conditions a lightweight MLP denoiser. I compared endpoint-only conditioning against a “Sparse Route” (a few breadcrumb waypoints). The results show that a sparse route is necessary to achieve tactical precision in complex turns.The model was tested on the Waymo Open Motion Dataset (WOMD) validation split.For comparison, a standard Behavioral Cloning MLP baseline typically reaches a minADE of around 0.81 on the same task. The latent diffusion approach achieves significantly better alignment with expert driving behavior.Hardware and ReproducibilityThe entire pipeline (data parsing, PCA computation, and training) runs on a single NVIDIA RTX 3090 (24GB VRAM). The code is structured to be used by other independent researchers who want to experiment with generative trajectory planning without industrial-scale hardware.I would appreciate any feedback on the latent space representation or the conditioning strategy. I am also interested in discussing how to integrate safety constraints directly into the denoising steps.